model governance

Leveraging the extensive experience with Financial Institutions, BRIDGEi2i has developed a model risk management platform, M². M² provides complete control and visibility over model performance and health with in-depth monitoring metrics for evaluation. M² effectively incorporates governance workflows to manage models across its entire lifecycle from development to retirement.

Model Monitoring is a branch of governance and regulations theory defined for financial services. It is proven that unregulated and ungoverned business often leads to inefficiency. More precisely, it studies the effects of changes in the internal and environmental systems at the macroscopic scale by analysing the collective motion of models using analytics. Though there are many important model monitoring principles that govern the behaviour of models, the most critical principles are defined in three basic laws of Model Monitoring,

Model Risk Management (MRM). One may wonder why talk about it now when it’s been nearly 3 years since the OCC guidelines were published. But before that let’s define MRM. It is the integrated process of managing model risk through a model’s entire lifecycle and in the process ensure a governance rhythm that is regulations compliant. Similar to OCC, various countries have framed regulations that could impact model governance. And this is a first in a series of blogs that can highlight the regulatory requirements and model governance parameters.

Corporates and Banks base their important decisions on data, such as to make profit, reduce cost or save themselves from making loss. They need to take many crucial decisions with respect to:

Investing in a new venture.Choice of consumers to target in a direct marketing campaignIdentifying fraudulent credit card transactions in real time.Answers to all the above business decisions are either a “Yes” or “No”. For example “Yes” to invest in a new venture, “Yes” to target a consumer, “Yes”, the credit card transaction is fraudulent.

This webcast is a recording of the recently conducted BRIDGEi2i Webinar – The Evolution from Model Validation to Model Governance for Banks

BRIDGEi2i Industry Experts, Anand Melige and Ashish Sharma discussed the growing importance of model risk management and the needs and challenges of decision makers in financial institutions in the present scenario.

In the relentless pursuit of competitive advantage, companies have adopted data driven decision making in a big way. A big manifestation of this is the proliferation of predictive models that are being deployed to lift business outcomes in a whole host of applications.

These range from trying to understand customers, segmentation and targeting, propensity to buy, forecasting, etc. etc. A typical firm would use 20 – 400+ models depending on portfolio size, number of products and analytics maturity.